Method and apparatus to identify object

ABSTRACT

A method and apparatus to identify an object include extracting first location information, second location information, and motion information of an object from a polarimetric RADAR signal that is reflected from the object. Each of the first location information, the second location information, and the motion information correspond to each of polarized waves. The apparatus and the method also include generating a first image and a second image, combining the first image and the second image to generate first composite images, each corresponding to each of the polarized waves, and identifying the object using a neural network based on the first composite images. The first image corresponds to each of the polarized waves and includes the first location information and the second location information, and the second image corresponds to each of the polarized waves and includes the first location information and the motion information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 USC § 119(a) of KoreanPatent Application No. 10-2017-0108469, filed on Aug. 28, 2017, in theKorean Intellectual Property Office, the entire disclosure of which isincorporated herein by reference for all purposes.

BACKGROUND 1. Field

The following description relates to an apparatus and a method ofidentifying an object using a neural network.

2. Description of Related Art

A quantity of data transmitted by a polarimetric RADAR is four timesthat of a single-polarization RADAR. In a polarimetric RADAR, requireddata is selectively used depending on the purpose of use. For example, arange-Doppler map includes range information and velocity information,and a micro-Doppler analysis includes velocity information.

SUMMARY

This Summary is provided to introduce a selection of concepts that arefurther described below in the Detailed Description in simplified form.This Summary is not intended to identify key features or essentialfeatures of the claimed subject matter, nor is it intended to be used asan aid in determining the scope of the claimed subject matter.

In accordance with an embodiment, there is provided a method ofidentifying an object, the method including: extracting first locationinformation, second location information, and motion information of anobject from a polarimetric RADAR signal that may be reflected from theobject, wherein each of the first location information, the secondlocation information, and the motion information correspond to each ofpolarized waves; generating a first image and a second image, whereinthe first image corresponds to each of the polarized waves and mayinclude the first location information and the second locationinformation, and the second image corresponds to each of the polarizedwaves and may include the first location information and the motioninformation; combining the first image and the second image to generatefirst composite images, each corresponding to each of the polarizedwaves; and identifying the object using a neural network based on thefirst composite images.

The method may be further include: generating a second composite imageby combining the first composite images to the neural network toidentify the object.

The first location information may be range information, the secondlocation information may be angle information, the first image may be arange-angle image, and the second image may be a range-velocity image.

The first location information may be vertical direction information,the second location information may be horizontal direction information,the first image may be a vertical direction-horizontal direction image,and the second image may be a vertical direction-velocity image.

The method may be further include: in response to determining that asignal-to-noise ratio (SNR) of the polarimetric RADAR signal satisfies acriterion, determining the first location information to be rangeinformation, determining the second location information to be angleinformation, determining the first image to be a range-angle image, anddetermining the second image to be a range-velocity image, and inresponse to determining that the SNR being does not satisfy thecriterion, determining the first location information to be verticaldirection information, determining the second location information to behorizontal direction information, determining the first image to be avertical direction-horizontal direction image, and determining thesecond image to be a vertical direction-velocity image.

The polarimetric RADAR signal may include a vertical/vertical (V/V)polarization signal, a vertical/horizontal (V/H) polarization signal, ahorizontal/vertical (H/V) polarization signal and ahorizontal/horizontal (H/H) polarization signal.

The polarimetric RADAR signal may include a left-handed circularpolarization (LHCP)/right-handed circular polarization (RHCP) signal, anLHCP/LHCP signal, an RHCP/LHCP signal, and an RHCP/RHCP signal.

The method of claim 1, may be further include: generating differentialimages between the first composite images; and generating a thirdcomposite image by combining the differential images to the neuralnetwork to identify the object.

The method may be further include: generating cross-correlation imagesbetween the first composite images; and generating a fourth compositeimage by combining the cross-correlation images to the neural network toidentify the object.

In accordance with an embodiment, there is provided a non-transitorycomputer-readable storage medium storing instructions that, whenexecuted by a processor, cause the processor to perform the methoddescribed above.

In accordance with an embodiment, there is provided an apparatus toidentify an object, the apparatus including: a processor configured toextract first location information, second location information, andmotion information of an object from a polarimetric RADAR signalreflected from the object, wherein each of the first locationinformation, the second location information and the motion informationcorrespond to each of polarized waves in a multi-polarization; generatea first image and a second image, wherein the first image corresponds toeach of the polarized waves and may include the first locationinformation and the second location information, and the second imagecorresponds to each of the polarized waves and may include the firstlocation information and the motion information;

combine the first image and the second image to generate first compositeimages, each corresponding to each of the polarized waves; and

identify the object using a neural network based on the first compositeimages.

The processor may be further configured to generate a second compositeimage by combining the first composite images to the neural network toidentify the object.

The first location information may be range information, the secondlocation information may be angle information, the first image may be arange-angle image, and the second image may be a range-velocity image.

The first location information may be vertical direction information,the second location information may be horizontal direction information,the first image may be a vertical direction-horizontal direction image,and the second image may be a vertical direction-velocity image.

The processor may be further configured to determine whether asignal-to-noise ratio (SNR) of the polarimetric RADAR signal satisfies apredetermined criterion, in response to the SNR being determined tosatisfy the criterion, the processor determines the first locationinformation to be range information, the second location information tobe angle information, the first image to be a range-angle image, and thesecond image to be a range-velocity image, and in response to the SNRbeing determined not to satisfy the criterion, the processor determinesthe first location information to be vertical direction information, thesecond location information to be horizontal direction information, thefirst image to be a vertical direction-horizontal direction image, andthe second image to be a vertical direction-velocity image.

The polarimetric RADAR signal may include a vertical/vertical (V/V)polarization signal, a vertical/horizontal (V/H) polarization signal, ahorizontal/vertical (H/V) polarization signal and ahorizontal/horizontal (H/H) polarization signal.

The polarimetric RADAR signal may include a left-handed circularpolarization (LHCP)/right-handed circular polarization (RHCP) signal, anLHCP/LHCP signal, an RHCP/LHCP signal, and an RHCP/RHCP signal.

The processor may be further configured to generate differential imagesbetween the first composite images, and to generate a third compositeimage by combining the differential images to the neural network toidentify the object.

The processor may be further configured to generate cross-correlationimages between the first composite images, and to generate a fourthcomposite image by combining the cross-correlation images to the neuralnetwork to identify the object.

The apparatus may be further include: a transmission antenna configuredto radiate the polarimetric RADAR signal to the object; and a receptionantenna configured to receive the reflected polarimetric RADAR signalfrom the object.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a configuration of a system to identifyan object.

FIG. 2 is a flowchart illustrating an example of a method of identifyingan object.

FIG. 3 is a flowchart illustrating another example of a method ofidentifying an object.

FIG. 4A illustrates an example of a range-angle image.

FIG. 4B illustrates an example of a range-angle image and arange-velocity image.

FIG. 4C illustrates an example of first composite images generated bycombining range-angle images and range-velocity images corresponding topolarized waves.

FIG. 5A illustrates an example of cross-correlation images correspondingto polarization pairs.

FIG. 5B illustrates an example of differential images corresponding topairs of polarization pairs.

Throughout the drawings and the detailed description, the same referencenumerals refer to the same elements. The drawings may not be to scale,and the relative sizes, proportions, and depictions of elements in thedrawings may be exaggerated for the purpose of clarity, illustration,and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent after an understanding of thedisclosure of this application. For example, the sequences of operationsdescribed herein are merely examples, and are not limited to those setforth herein, but may be changed as will be apparent after anunderstanding of the disclosure of this application, with the exceptionof operations necessarily occurring in a certain order. Also,descriptions of features that are known in the art may be omitted forincreased clarity and conciseness.

The features described herein may be embodied in different forms, andare not to be construed as being limited to the examples describedherein. Rather, the examples described herein have been provided merelyto illustrate some of the many possible ways of implementing themethods, apparatuses, and/or systems described herein that will beapparent after an understanding of the disclosure of this application.

The following structural or functional descriptions of examplesdisclosed in the present disclosure are merely intended for the purposeof describing the examples and the examples may be implemented invarious forms. The examples are not meant to be limited, but it isintended that various modifications, equivalents, and alternatives arealso covered within the scope of the claims.

Although terms of “first” or “second” are used to explain variouscomponents, the components are not limited to the terms. These termsshould be used only to distinguish one component from another component.For example, a “first” component may be referred to as a “second”component, or similarly, and the “second” component may be referred toas the “first” component within the scope of the right according to theconcept of the present disclosure.

It will be understood that when a component is referred to as being“connected to” another component, the component can be directlyconnected or coupled to the other component or intervening componentsmay be present.

As used herein, the singular forms are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It shouldbe further understood that the terms “comprises” and/or “comprising,”when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, components or acombination thereof, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

Unless otherwise defined herein, all terms used herein includingtechnical or scientific terms have the same meanings as those generallyunderstood. Terms defined in dictionaries generally used should beconstrued to have meanings matching with contextual meanings in therelated art and are not to be construed as an ideal or excessivelyformal meaning unless otherwise defined herein.

Hereinafter, examples will be described in detail with reference to theaccompanying drawings. In the following description, it should be notedthat the same elements will be designated by the same referencenumerals, wherever possible, even though they are shown in differentdrawings.

FIG. 1 illustrates an example of a configuration of a system to identifyan object.

Referring to FIG. 1, an object identification apparatus 100 uses asingle-channel image by using a neural network, and generates acomposite image including an amount of information sufficient toidentify an object 130 by combining velocity information with thesingle-channel image. The object identification apparatus 100 identifiesthe object 130 based on the composite image.

The object identification apparatus 100 includes a transmission antenna121, a reception antenna 123 and a processor 110. The transmissionantenna 121 radiates or transmits a polarimetric RADAR signal to anobject 130. The reception antenna 123 receives a polarimetric RADARsignal that is reflected from the object 130 and that returns to theobject identification apparatus 100. The processor 110 identifies alocation or shape of the object 130 based on the received polarimetricRADAR signal. In the present disclosure, the term “RADAR” is an acronymfor “radio detection and ranging.”

The object identification apparatus 100 generates a single-channel imagefrom pieces of location information for each polarized wave based on thepolarimetric RADAR signal, and generates a two-channel image bycombining the single-channel image with velocity information. The objectidentification apparatus 100 generates a single image by combiningtwo-channel images for each polarized wave, inputs the generated imageto a pre-trained neural network, and identifies an object represented bythe image.

The neural network is referred to as an “artificial neural network.” Theneural network is a recognition model or a recognition process or methodimplemented by hardware that mimics a calculation ability of abiological system using a large number of artificial neurons (or nodes).The neural network performs a human cognition or learning process usingthe artificial neurons. The neural network includes, for example, a deepneural network, a convolutional neural network or a recurrent neuralnetwork. For example, the neural network is trained using or based ontraining data acquired through an error backpropagation algorithm.

In an example, the polarimetric RADAR signal includes avertical/vertical (V/V) polarization signal, a vertical/horizontal (V/H)polarization signal, a horizontal/vertical (H/V) polarization signal anda horizontal/horizontal (H/H) polarization signal. In another example,the polarimetric RADAR signal includes a left-handed circularpolarization (LHCP)/right-handed circular polarization (RHCP) signal, anLHCP/LHCP signal, an RHCP/LHCP signal, and an RHCP/RHCP signal.Different types of polarized signals or waves do not affect each other.

The object identification apparatus 100 extracts range information,angle information or velocity information from the polarimetric RADARsignal, for each polarized wave. A type of polarizations includes, forexample, four pairs of vertical polarizations and horizontalpolarizations, or four pairs of LHCPs and RHCPs.

In an example, the object identification apparatus 100 generates atwo-dimensional (2D) range-angle image based on the range informationand the angle information for each polarized wave. A range-angle imageincludes, for example, a real aperture RADAR (RAR) image. In anotherexample, the object identification apparatus 100 generates a 2D verticaldistance-horizontal distance image based on vertical distanceinformation and horizontal distance information for each polarized wave.A vertical distance-horizontal distance image includes, for example, asynthetic-aperture RADAR (SAR) image.

Also, the object identification apparatus 100 generates athree-dimensional (3D) first composite image by synthesizing thevelocity information with a 2D image. The object identificationapparatus 100 inputs the 3D first composite image to a neural networktrained through deep learning, and identifies an object represented inthe 3D first composite image. An artificial intelligence function thatimitates the workings of the human brain in processing data and creatingpatterns for use in decision making. Deep learning is a subset ofmachine learning in Artificial Intelligence (AI) that has networks whichare capable of learning unsupervised from data that is unstructured orunlabeled. An image processing algorithm, according to a related art,uses a three-channel image including RGB information and, as a result,data loss occurs due to a polarimetric RADAR signal that providesfour-channel information. The object identification apparatus 100, inaccordance with an example of the present disclosure, processes asingle-channel image using the neural network and, thus, the objectidentification apparatus 100 makes it possible to select desiredinformation from a polarimetric RADAR signal and to reduce data loss.

The object identification apparatus 100 generates a two-channel image byreflecting the velocity information in the single-channel image. Thus,the object identification apparatus 100 synthesizes the velocityinformation with an insufficient amount of information in thesingle-channel image to provide a two-channel image including a moresufficient amount of information to the neural network. Thus, anaccuracy of an object identification result of the neural network isenhanced.

In another example, the object identification apparatus 100 increases anumber of transmitters of a polarimetric RADAR and a number of receiversof the polarimetric RADAR. The object identification apparatus 100extracts more diverse information from polarimetric RADAR signals usinga multiple-input and multiple-output (MIMO) technology and a virtualarray technology.

FIG. 2 illustrates an example of a method of identifying an object.

Referring to FIG. 2, in operation 210, the object identificationapparatus 100 of FIG. 1 extracts first location information, secondlocation information and motion information of an object from apolarimetric RADAR signal that is reflected from the object and thatreturns to the object identification apparatus 100. Each of the firstlocation information, the second location information, and the motioninformation corresponds to each of polarized waves in amulti-polarization. For example, the object identification apparatus 100receives a signal of each of the polarized waves via a polarimetricRADAR antenna, stores the signal as a digital signal, and extractsinformation, for example, a range, an angle or a velocity, from signalsfor each of the polarized waves, using various processes.

In operation 220, the object identification apparatus 100 generates afirst image and a second image. The first image corresponds to each ofthe polarized waves, and includes the first location information and thesecond location information. The second image corresponds to each of thepolarized waves, and includes the first location information and themotion information.

For example, when the polarimetric RADAR signal includes avertical/vertical (V/V) polarization signal, a vertical/horizontal (V/H)polarization signal, a horizontal/vertical (H/V) polarization signal,and a horizontal/horizontal (H/H) polarization signal, the objectidentification apparatus 100 generates four first images for each of thepolarized waves. In this example, each of the four first images has asingle channel, instead of three channels, for example, RGB channels.The object identification apparatus 100 synthesizes velocity informationwith each of the four first images. As a result, four second images oftwo channels, that is, a channel of image information and a channel ofvelocity information are generated.

In an example, when the first location information is range informationand when the second location information is angle information, the firstimage is a range-angle image and the second image is range-velocityimage. In this example, the first image is an RAR image.

In another example, when the first location information is verticaldistance information and when the second location information ishorizontal distance information, the first image is a verticaldistance-horizontal distance image, and the second image is a verticaldirection-velocity image. In this example, the first image is an SARimage.

In operation 230, the object identification apparatus 100 generatesfirst composite images each corresponding to each of the polarized wavesby combining the first image and the second image.

In operation 250, the object identification apparatus 100 identifies theobject using a neural network based on the first composite images.

The object identification apparatus 100 generates a second compositeimage by combining the first composite images. For example, the objectidentification apparatus generates a second composite image of eightchannels by combining four first composite images of two channels. Theobject identification apparatus 100 inputs the second composite image tothe neural network and identifies the object.

In operation 240, the object identification apparatus 100 generatesdifferential images between the first composite images orcross-correlation images between the first composite images. The objectidentification apparatus 100 generates a third composite image bycombining differential images. The object identification apparatus 100inputs the third composite image to the neural network and identifiesthe object.

The object identification apparatus 100 generates a fourth compositeimage by combining cross-correlation images. The object identificationapparatus 100 inputs the fourth composite image to the neural networkand identifies the object. The neural network has a structure to receivea multi-channel image generated by synthesizing or combiningpolarization information, velocity information, or additionalinformation.

FIG. 3 illustrates another example of a method of identifying an object.

Referring to FIG. 3, in operation 310, the object identificationapparatus 100 of FIG. 1 extracts first location information, secondlocation information, and motion information of an object from apolarimetric RADAR signal that is reflected from the object and thatreturns to the object identification apparatus 100. Each of the firstlocation information, the second location information, and the motioninformation corresponds to each of polarized waves in amulti-polarization.

In operation 320, the object identification apparatus 100 determineswhether a signal-to-noise ratio (SNR) of the polarimetric RADAR signalsatisfies a predetermined criterion. For example, the objectidentification apparatus 100 determines whether an SAR image with a highaccuracy is required or a range-angle image that allows a fast detectionis required.

The object identification apparatus 100 generates an SAR image or arange-angle image based on situation information including an object.For example, the situation information includes a propagationenvironment or a number of targets, and the propagation environment isrepresented by the SNR of the polarimetric RADAR signal. The SAR imageand the range-angle image are different from each other in a resolutionand a processing speed. The object identification apparatus 100 uses arange-angle image-based algorithm to obtain a fast result, and uses anSAR image to obtain an accurate result.

In operation 331, in response to the SNR satisfying the criterion, theobject identification apparatus 100 generates a range-angle image and arange-velocity image. The range-angle image corresponds to each of thepolarized waves and includes range information and angle informationthat each correspond to each of the polarized waves. The range-velocityimage corresponds to each of the polarized waves and includes the rangeinformation and the motion information.

The range-angle image is, for example, an RAR image. When the SNRsatisfies the criterion, the range information is the first locationinformation, the angle information is the second location information,the range-angle image is generated as a first image, and therange-velocity image is generated as a second image. The criterion isused to determine whether the SNR is less than a threshold. In operation341, the object identification apparatus 100 generates first compositeimages each corresponding to each of the polarized waves by combiningthe range-angle image and the range-velocity image. In operation 351,the object identification apparatus 100 generates differential images orcross-correlation images between the first composite images. Inoperation 361, the object identification apparatus 100 identifies theobject using a neural network based on the first composite images. Forexample, the object identification apparatus 100 inputs the firstcomposite images to the neural network, and identifies the object.

In operation 333, in response to the SNR not satisfying the criterion,the object identification apparatus 100 generates a verticaldistance-horizontal distance image and a vertical direction-velocityimage. The vertical distance-horizontal distance image corresponds toeach of the polarized waves, and includes vertical distance informationand horizontal distance information that each correspond to each of thepolarized waves. The vertical direction-velocity image corresponds toeach of the polarized waves, and includes the vertical distanceinformation and the motion information. The vertical distance-horizontaldistance image is, for example, an SAR image. When the SNR does notsatisfy the criterion, the vertical distance information is the firstlocation information, the horizontal distance information is the secondlocation information, the vertical distance-horizontal distance image isgenerated as a first image, and the vertical direction-velocity image isgenerated as a second image. The criterion is used to determine whetherthe SNR is less than a threshold.

In operation 343, the object identification apparatus 100 generatesfirst composite images each corresponding to each of the polarized wavesby combining the vertical distance-horizontal distance image and thevertical direction-velocity image. In operation 353, the objectidentification apparatus 100 generates differential images orcross-correlation images between the first composite images. Inoperation 363, the object identification apparatus 100 identifies theobject using a neural network based on the first composite images. Forexample, the object identification apparatus 100 inputs the firstcomposite images to the neural network, and identifies the object.

FIG. 4A illustrates an example of a range-angle image. FIG. 4Billustrates an example of a range-angle image and a range-velocityimage. FIG. 4C illustrates an example of first composite imagesgenerated by combining range-angle images and range-velocity imagescorresponding to polarized waves.

Referring to FIG. 4A, an object identification apparatus generates a 2Drange-angle image 410 based on range information and angle informationfor each of polarized waves. Referring to FIG. 4B, the objectidentification apparatus generates a 3D first composite image bysynthesizing velocity information with the 2D range-angle image 410. Thevelocity information is included in a form of a 2D image in arange-velocity image 420.

Referring to FIG. 4C, the object identification apparatus generates 3Dfirst composite images 431, 433 and 435. The first composite images 431,433 and 435 each corresponds to the polarized waves. For example, thefirst composite image 431 corresponds to a V/V polarized wave, the firstcomposite image 433 corresponds to a V/H polarized wave, and the firstcomposite image 435 corresponds to an H/H polarized wave.

Although the first composite images 431, 433 and 435 are expressed in 2Das shown in FIG. 4C, each of the first composite images 431, 433 and 435may be expressed as a 3D image including range information, angleinformation, and velocity information.

FIG. 5A illustrates examples of cross-correlation images correspondingto polarization pairs. FIG. 5B illustrates examples of differentialimages corresponding to polarization pairs.

An object identification apparatus generates a cross-correlation imagebetween composite images. A cross-correlation is a degree of acorrelation between different signals. The cross-correlation image is animage generated by setting a correlation between pixels corresponding totwo different images as a new pixel value. The object identificationapparatus generates a cross-correlation image between composite imagescorresponding to polarization pairs.

FIG. 5A illustrates cross-correlation images 501 and 503. Thecross-correlation image 501 is a cross-correlation image between thefirst composite images 431 and 433. The cross-correlation image 503 is across-correlation image between the first composite images 433 and 435.For example, the first composite image 431 corresponds to a V/Vpolarized wave, the first composite image 433 corresponds to a V/Hpolarized wave, and the first composite image 435 corresponds to an H/Hpolarized wave.

The object identification apparatus generates a differential imagebetween composite images. A difference between pixel values of twocomposite images is set as a new pixel value, and the differential imageis generated. For example, the object identification apparatus generatesa differential image between composite images corresponding to each ofpolarization pairs.

Referring to FIG. 5B, a differential image 505 is a differential imagebetween the first composite images 431 and 433. A differential image 507is a differential image between the first composite images 433 and 435.

The object identification apparatus 100, the transmission antenna 121,the reception antenna 123, the processor 110 and other apparatuses,units, modules, devices, and other components described herein withrespect to FIG. 1 are implemented by hardware components. Examples ofhardware components that may be used to perform the operations describedin this application where appropriate include controllers, sensors,generators, drivers, memories, comparators, arithmetic logic units,adders, subtractors, multipliers, dividers, integrators, and any otherelectronic components configured to perform the operations described inthis application. In other examples, one or more of the hardwarecomponents that perform the operations described in this application areimplemented by computing hardware, for example, by one or moreprocessors or computers. A processor or computer may be implemented byone or more processing elements, such as an array of logic gates, acontroller and an arithmetic logic unit, a digital signal processor, amicrocomputer, a programmable logic controller, a field-programmablegate array, a programmable logic array, a microprocessor, or any otherdevice or combination of devices that is configured to respond to andexecute instructions in a defined manner to achieve a desired result. Inone example, a processor or computer includes, or is connected to, oneor more memories storing instructions or software that are executed bythe processor or computer. Hardware components implemented by aprocessor or computer may execute instructions or software, such as anoperating system (OS) and one or more software applications that run onthe OS, to perform the operations described in this application. Thehardware components may also access, manipulate, process, create, andstore data in response to execution of the instructions or software. Forsimplicity, the singular term “processor” or “computer” may be used inthe description of the examples described in this application, but inother examples multiple processors or computers may be used, or aprocessor or computer may include multiple processing elements, ormultiple types of processing elements, or both. For example, a singlehardware component or two or more hardware components may be implementedby a single processor, or two or more processors, or a processor and acontroller. One or more hardware components may be implemented by one ormore processors, or a processor and a controller, and one or more otherhardware components may be implemented by one or more other processors,or another processor and another controller. One or more processors, ora processor and a controller, may implement a single hardware component,or two or more hardware components. A hardware component may have anyone or more of different processing configurations, examples of whichinclude a single processor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIGS. 2 and 3 that perform the operationsdescribed in this application are performed by computing hardware, forexample, by one or more processors or computers, implemented asdescribed above executing instructions or software to perform theoperations described in this application that are performed by themethods. For example, a single operation or two or more operations maybe performed by a single processor, or two or more processors, or aprocessor and a controller. One or more operations may be performed byone or more processors, or a processor and a controller, and one or moreother operations may be performed by one or more other processors, oranother processor and another controller. One or more processors, or aprocessor and a controller, may perform a single operation, or two ormore operations.

Instructions or software to control a processor or computer to implementthe hardware components and perform the methods as described above arewritten as computer programs, code segments, instructions or anycombination thereof, for individually or collectively instructing orconfiguring the processor or computer to operate as a machine orspecial-purpose computer to perform the operations performed by thehardware components and the methods as described above. In one example,the instructions or software include machine code that is directlyexecuted by the processor or computer, such as machine code produced bya compiler. In another example, the instructions or software includehigher-level code that is executed by the processor or computer using aninterpreter. Programmers of ordinary skill in the art can readily writethe instructions or software based on the block diagrams and the flowcharts illustrated in the drawings and the corresponding descriptions inthe specification, which disclose algorithms for performing theoperations performed by the hardware components and the methods asdescribed above.

The instructions or software to control a processor or computer toimplement the hardware components and perform the methods as describedabove, and any associated data, data files, and data structures, arerecorded, stored, or fixed in or on one or more non-transitorycomputer-readable storage media. Examples of a non-transitorycomputer-readable storage medium include read-only memory (ROM),random-access programmable read only memory (PROM), electricallyerasable programmable read-only memory (EEPROM), random-access memory(RAM), dynamic random access memory (DRAM), static random access memory(SRAM), flash memory, non-volatile memory, CD-ROMs, CD-Rs, CD+Rs,CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs,BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, blue-ray or optical disk storage,hard disk drive (HDD), solid state drive (SSD), flash memory, a cardtype memory such as multimedia card micro or a card (for example, securedigital (SD) or extreme digital (XD)), magnetic tapes, floppy disks,magneto-optical data storage devices, optical data storage devices, harddisks, solid-state disks, and any other device that is configured tostore the instructions or software and any associated data, data files,and data structures in a non-transitory manner and providing theinstructions or software and any associated data, data files, and datastructures to a processor or computer so that the processor or computercan execute the instructions.

While this disclosure includes specific examples, it will be apparent toone of ordinary skill in the art that various changes in form anddetails may be made in these examples without departing from the spiritand scope of the claims and their equivalents. The examples describedherein are to be considered in a descriptive sense only, and not forpurposes of limitation. Descriptions of features or aspects in eachexample are to be considered as being applicable to similar features oraspects in other examples. Suitable results may be achieved if thedescribed techniques are performed in a different order, and/or ifcomponents in a described system, architecture, device, or circuit arecombined in a different manner, and/or replaced or supplemented by othercomponents or their equivalents. Therefore, the scope of the disclosureis defined not by the detailed description, but by the claims and theirequivalents, and all variations within the scope of the claims and theirequivalents are to be construed as being included in the disclosure.

What is claimed is:
 1. A method of identifying an object, the methodcomprising: extracting first location information, second locationinformation, and motion information of an object from a polarimetricRADAR signal that is reflected from the object, wherein each of thefirst location information, the second location information, and themotion information correspond to each of polarized waves in a multipolarization; generating a first image comprising the first locationinformation and the second location information for the each of thepolarized waves; generating a second image comprising the first locationinformation and the motion information for the each of the polarizedwaves; combining the first image and the second image for each of therespective polarized waves to generate first composite images; andidentifying the object using a neural network based on the firstcomposite images.
 2. The method of claim 1, wherein the identifying ofthe object comprises: inputting a second composite image generated bycombining the first composite images to the neural network to identifythe object.
 3. The method of claim 1, wherein the first locationinformation is range information, the second location information isangle information, the first image is a range-angle image, and thesecond image is a range-velocity image.
 4. The method of claim 1,wherein the first location information is vertical directioninformation, the second location information is horizontal directioninformation, the first image is a vertical direction-horizontaldirection image, and the second image is a vertical direction-velocityimage.
 5. The method of claim 1, further comprising: in response todetermining that a signal-to-noise ratio (SNR) of the polarimetric RADARsignal satisfies a criterion, determining the first location informationto be range information, determining the second location information tobe angle information, determining the first image to be a range-angleimage, and determining the second image to be a range-velocity image,and in response to determining that the SNR being does not satisfy thecriterion, determining the first location information to be verticaldirection information, determining the second location information to behorizontal direction information, determining the first image to be avertical direction-horizontal direction image, and determining thesecond image to be a vertical direction-velocity image.
 6. The method ofclaim 1, wherein the polarimetric RADAR signal comprises avertical/vertical (V/V) polarization signal, a vertical/horizontal (V/H)polarization signal, a horizontal/vertical (H/V) polarization signal anda horizontal/horizontal (H/H) polarization signal.
 7. The method ofclaim 1, wherein the polarimetric RADAR signal comprises a left-handedcircular polarization (LHCP)/right-handed circular polarization (RHCP)signal, an LHCP/LHCP signal, an RHCP/LHCP signal, and an RHCP/RHCPsignal.
 8. The method of claim 1, further comprising: generatingdifferential images between the first composite images; and inputting athird composite image generated by combining the differential images tothe neural network to identify the object.
 9. The method of claim 1,further comprising: generating cross-correlation images between thefirst composite images; and inputting a fourth composite image generatedby combining the cross-correlation images to the neural network toidentify the object.
 10. A non-transitory computer-readable storagemedium storing instructions that, when executed by a processor, causethe processor to perform the method of claim
 1. 11. An apparatus toidentify an object, the apparatus comprising: a processor configured toextract first location information, second location information, andmotion information of an object from a polarimetric RADAR signalreflected from the object, wherein each of the first locationinformation, the second location information and the motion informationcorrespond to each of polarized waves in a multi-polarization; generatea first image comprising the first location information and the secondlocation information for the each of the polarized waves; generate asecond image comprising the first location information and the motioninformation for the each of the polarized waves; combine the first imageand the second image for the each of the respective polarized waves togenerate first composite images; and identify the object using a neuralnetwork based on the first composite images.
 12. The apparatus of claim11, wherein the processor is further configured to input a secondcomposite image generated by combining the first composite images to theneural network to identify the object.
 13. The apparatus of claim 11,wherein the first location information is range information, the secondlocation information is angle information, the first image is arange-angle image, and the second image is a range-velocity image. 14.The apparatus of claim 11, wherein the first location information isvertical direction information, the second location information ishorizontal direction information, the first image is a verticaldirection-horizontal direction image, and the second image is a verticaldirection-velocity image.
 15. The apparatus of claim 11, wherein theprocessor is further configured to determine whether a signal-to-noiseratio (SNR) of the polarimetric RADAR signal satisfies a predeterminedcriterion, in response to the SNR being determined to satisfy thecriterion, the processor determines the first location information to berange information, the second location information to be angleinformation, the first image to be a range-angle image, and the secondimage to be a range-velocity image, and in response to the SNR beingdetermined not to satisfy the criterion, the processor determines thefirst location information to be vertical direction information, thesecond location information to be horizontal direction information, thefirst image to be a vertical direction-horizontal direction image, andthe second image to be a vertical direction-velocity image.
 16. Theapparatus of claim 11, wherein the polarimetric RADAR signal comprises avertical/vertical (V/V) polarization signal, a vertical/horizontal (V/H)polarization signal, a horizontal/vertical (H/V) polarization signal anda horizontal/horizontal (H/H) polarization signal.
 17. The apparatus ofclaim 11, wherein the polarimetric RADAR signal comprises a left-handedcircular polarization (LHCP)/right-handed circular polarization (RHCP)signal, an LHCP/LHCP signal, an RHCP/LHCP signal, and an RHCP/RHCPsignal.
 18. The apparatus of claim 11, wherein the processor is furtherconfigured to generate differential images between the first compositeimages, and to input a third composite image generated by combining thedifferential images to the neural network to identify the object. 19.The apparatus of claim 11, wherein the processor is further configuredto generate cross-correlation images between the first composite images,and to input a fourth composite image generated by combining thecross-correlation images to the neural network to identify the object.20. The apparatus of claim 11, further comprising: a transmissionantenna configured to radiate the polarimetric RADAR signal to theobject; and a reception antenna configured to receive the reflectedpolarimetric RADAR signal from the object.